Shadowing Practice: Controlled Multi-modal Image Generation for Plant Growth Modeling - Learn English Speaking with Video

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Hello my name is Libana Roscher
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and I want to present our paper Controlled multimodal image generation for plant growth modeling
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which was presented at the international conference on pattern recognition last summer.
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So when we have a look at such an aquaculture field
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and we work with robotics and with machine learning methods for phenotyping
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we can ask ourselves different questions
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so for example we can ask what is on the field
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which is a typical classification task
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or we can ask what is the yield in a specific area
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which is a typical regression task
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or we can ask ourselves what will a plant look like in the future
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and this is a forecasting tasks
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and this is exactly the question we addressed in our paper
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so imagine the following scenario you already know that
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when plants grow closely together this can be very beneficial
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so for example it can increase the biomass the crucial question is now what is actually a good mixture
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and so what you can do is you can test different mixtures then you can collect data
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and then you can learn a model from this information
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and this model relates the information from what we have measured to a parameter like the biomass
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so you have a relationship between the mixtures and the biomass
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and then the good thing is this model can be applied to new mixtures
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and these new mixtures were not tested yet in the field before
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so you get an idea what potential good mixtures are
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And one problem is now that one might be interested in the biomass
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and the other might be interested in the spread of diseases.
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So it would be good to combine this and to get complete information.
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And this can be done if you model the whole appearance with plants, that means if you generate images.
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And with this you are flexible in what you do with the result
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because it can be treated such as an artificial sensor measurement.
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So in order to generate such images we used
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so -called generative adversarial networks illustrated here and this is a neural network consisting of two parts.
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So one part is the generator G and this generator is responsible to generate images.
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The other part is the discriminator, and the discriminator helps to train the generator to generate really good images.
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The discriminator does it by trying to distinguish between real images and images which were generated by the generator.
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So in the best case, the generator generates such good images
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that the discriminator can no longer distinguish between a real image
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and an image which was generated but there's one crucial thing
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which is important for our application
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so we do not want only want to generate an arbitrary
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nice -looking image we want to condition on specific factors on specific things
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and one important thing is we want to condition on the appearance of an of an additional input image
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which is from an earlier point in time
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and the second important thing is we want to condition on
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so -called factors of variation and these factors of variation are factors
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which influence the appearance of the of the plants
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and the novelty of our paper is exactly this part
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because on the one hand we learned the representation
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which is useful for the machine learning model but on the other side it was
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Because we can also easily and intuitively change these factors of variation so that we can produce different scenarios, different outcomes.
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And so here you can see results of
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which we presented in our paper where we generated images with chain factors of variation.
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So our dataset contains UAV images, which were captures in Kampus -Klein -Altendorf,
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where we have different mixtures of spring wheat and faba bean.
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And we also collected different factors of variation, which belong to these mixtures.
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And they are illustrated in green and light blue and in dark blue.
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And on the left, you can see the input image, which is from an early growth stage.
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And this is the real scene from a later growth stage and the measured factors of variation.
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And these images are all generated images.
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So here we change the factors of variation.
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So for example, on the very right image, you can see we set all factors of variation for Faba bean to zero.
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And actually there are no Faba bean plants in the generated image.
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So we can clearly control and manipulate how the images can be generated it
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and from these images we can now derive specific parameters pointing
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us to potential promising mixtures we can later test in the
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field with this thank you very much for listening you can find the paper by scanning the qr code
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and if you are more interested in this research direction you can go to our website
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or you can have a look in my YouTube channel.
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Thanks a lot!

What You'll Learn

This video is a goldmine for English speaking practice, especially if you’re prepping for IELTS speaking or honing shadowing skills. You’ll master how to explain complex ideas clearly—like describing technical research (generative adversarial networks!) in conversational English. You’ll also learn to connect ideas smoothly, using phrases like “so for example” and “and this is exactly the question we addressed” to guide listeners. Plus, you’ll practice structuring a presentation: starting with an introduction, posing questions, and outlining solutions—key for formal speaking tasks.

Listen For These Sounds

Notice the connected speech in phrases like “what is on the field” (where “what is” links to “wuh - tiz”) and “interested in the biomass” (where “in the” becomes “in thuh”). The speaker also uses reductions, such as “it can be” shortened to “it can be” (with a relaxed “be” sound). Pay attention to how “mixtures” is pronounced with a soft “t” (like “mixchurs”)—a common feature in natural speech. These links and reductions will help you sound more fluent when shadowing.

Say It Like a Native

To mirror the speaker’s rhythm, stress key terms like “controlled multimodal image generation,” “generative adversarial networks,” and “factors of variation”—these are the ideas she wants to emphasize. Notice her pacing: she pauses briefly after questions (“so what can you do?”) to let ideas sink in, then speeds up slightly when explaining processes. Practice shadowing her using a shadowing app or site—repeat her sentences immediately after, matching her stress and pauses. This will help you build natural intonation and confidence, whether you’re practicing for IELTS or everyday conversations. Remember: the goal is to sound clear and engaging, not just fast!

¿Qué es la Técnica de Shadowing?

Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.